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Minimum classification error training of hidden Markov models for handwriting recognition
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
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This paper evaluates the application of the Minimum Classification Error (MCE) training to online-handwritten text recognition based on Hidden Markov Models. We describe an allograph-based, character level MCE training aimed at minimizing the character error rate while enabling flexibility in writing style. Experiments on a writer-independent discrete character recognition task covering all alpha-numerical characters and keyboard symbols show that MCE achieves more than 30% character error rate
doi:10.1109/icassp.2001.941223
dblp:conf/icassp/Biem01
fatcat:uh7lszwr2febjpfiq3myzb3j3q